Patrolling-Zoo offers a flexible framework enabling users to create and experiment with multi-agent patrolling tasks in Python. The library includes a variety of grid-based and graph-based environments, each simulating surveillance, monitoring, and coverage scenarios. Users can configure the number of agents, map size, topology, reward functions, and observation spaces. Through compatibility with PettingZoo and Gym APIs, it supports seamless integration with popular reinforcement learning algorithms. This environment facilitates benchmarking and comparing MARL techniques under consistent settings. By providing standard scenarios and tools to customize new ones, Patrolling-Zoo accelerates research in autonomous robotics, security surveillance, search-and-rescue operations, and efficient area coverage using multi-agent coordination strategies.

Anomali Copilot acts as an intelligent assistant for cybersecurity teams, leveraging advanced AI and natural language processing to assist in the evaluation of threats. It enables users at any skill level to conduct complex analyses of cybersecurity data. By automating the detection of imminent threats and providing contextual intelligence, it helps organizations remain vigilant against potential cyber risks. This tool not only enhances individual performance but also enables broader team collaboration, streamlining workflows within Security Operations Centers (SOCs).